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Modeling Covariance Breakdowns in Multivariate GARCH

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  • Jin, Xin
  • Maheu, John M

Abstract

This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and implied correlation coefficients. Different breakdown periods will have different impacts on the conditional covariance matrix and are estimated from the data. We propose an efficient Bayesian posterior sampling procedure for the estimation and show how to compute the marginal likelihood of the model. When applying the model to daily stock market and bond market data, we identify a number of different covariance breakdowns. Modeling covariance breakdowns leads to a significant improvement in the marginal likelihood and gains in portfolio choice.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 55243.

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Date of creation: 10 Apr 2014
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Handle: RePEc:pra:mprapa:55243

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Keywords: correlation breakdown; marginal likelihood; particle filter; Markov chain; generalized variance;

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